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Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm.

Vani Rajasekar1, Bratislav Predić2, Muzafer Saracevic3

  • 1Department of CSE, Kongu Engineering College, Perundurai, Erode, India.

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This study introduces an enhanced multimodal biometric technique for smart cities, utilizing an optimized fuzzy genetic algorithm for score-level fusion. The new method significantly improves accuracy and recognition rates for secure data access.

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Area of Science:

  • Computer Science
  • Data Security
  • Artificial Intelligence

Background:

  • Biometric security is crucial for data protection, with rapidly increasing research.
  • Multimodal biometrics offer enhanced accuracy but face challenges in smart city applications.
  • Existing techniques struggle to balance accuracy and recognition rates effectively.

Purpose of the Study:

  • To propose an enhanced multimodal biometric technique for smart city applications.
  • To address challenges in accuracy and recognition rates using score-level fusion.
  • To improve biometric security through an optimized fuzzy genetic algorithm.

Main Methods:

  • Score-level fusion of multiple biometric traits.
  • Implementation of an optimized fuzzy genetic algorithm for fusion.
  • Experimental validation across diverse biometric environments.

Main Results:

  • Achieved a high accuracy rate of 99.88%.
  • Demonstrated a low equal error rate of 0.18%.
  • Showed significant performance improvements over existing biometric strategies.

Conclusions:

  • The proposed optimized fuzzy genetic algorithm enhances multimodal biometric performance.
  • This technique offers a robust solution for secure smart city data.
  • The approach provides superior accuracy and reduced error rates.